ovbias function

Compute bias adjusted treatment effect taking parameter vector as input.

ovbias(parameters, deltalow, deltahigh, Rhigh, e)

Arguments

• parameters: A vector of parameters (real numbers) that is generated by estimating the short, intermediate and auxiliary regressions.
• deltalow: The lower limit of delta.
• deltahigh: The upper limit of delta.
• Rhigh: The upper limit of Rmax.
• e: The step size.

Returns

List with three elements:

• Data: Data frame containing the bias ($bias) and bias-adjusted treatment effect ($bstar) for each point on the grid

• bias_Distribution: Quantiles (2.5,5.0,50,95,97.5) of the empirical distribution of bias

• bstar_Distribution: Quantiles (2.5,5.0,50,95,97.5) of the empirical distribution of the bias-adjusted treatment effect

Examples

## Load data set
data("NLSY_IQ")
 
## Set age and race as factor variables
NLSY_IQ$age <- factor(NLSY_IQ$age)
NLSY_IQ$race <- factor(NLSY_IQ$race)
   
## Collect parameters from the short, intermediate and auxiliary regressions
parameters <- collect_par(
data = NLSY_IQ, outcome = "iq_std", 
treatment = "BF_months", 
control = c("age","sex","income","motherAge","motherEDU","mom_married","race"),
other_regressors = c("sex","age"))

## Set limits for the bounded box
Rlow <- parameters$Rtilde
Rhigh <- 0.61
deltalow <- 0.01
deltahigh <- 0.99
e <- 0.01

## Not run:

## Compute bias and bias-adjusted treatment effect
OVB <- ovbias(
parameters = parameters, 
deltalow=deltalow, 
deltahigh=deltahigh, Rhigh=Rhigh, 
e=e)

## Default quantiles of bias
(OVB$bias_Distribution)

## Chosen quantilesof bias
quantile(OVB$Data$bias, c(0.01,0.05,0.1,0.9,0.95,0.975))

## Default quantiles of bias-adjusted treatment effect
(OVB$bstar_Distribution)

## Chosen quantiles of bias-adjusted treatment effect
quantile(OVB$Data$bstar, c(0.01,0.05,0.1,0.9,0.95,0.975))
## End(Not run)